There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Brittany Intzandt |
| Institution: | Sunnybrook Research Institute |
| Department: | Hurvitz Brain Sciences Program BrainLab |
| Country: | |
| Proposed Analysis: | Title: Sex-specific risk factors during aging and the dementia spectrum: an in depth understanding using network analysis Brief description: The number of individual's with Alzheimer's disease, is on the rise and will continue to increase as the number of older adults that comprise our populations are expected to continue to climb. Identifying risk factors that contribute most and the interaction of them, is necessary to have efficacious preventative and interventional strategies. Importantly, sex is well documented to have significant effects on physiology throughout the lifespan, yet in the context of risk factors in the aging and dementia spectrum is fairly understudied. When it is involved in an analysis, sex-aggregated analyses (i.e., employing sex as a covariate) are utilized, rather than employing sex-disaggregated analyses. Here, we propose to employ a network approach to identify how males and females risk factors across the spectrum of dementia differ and overlap. Background: The risk of being diagnosed with a neurodegenerative disease, like Alzheimer's disease (AD) increases with aging due to an assortment of modifiable and non-modifiable risk factors. Risk factors (RF) for AD include age, genetics, vascular risk factors, to name a few. Sex differences for the risk factors of AD are not well characterized, as most studies use sex as a covariate, rather than sex-disaggregated analyses. For example, hypertension in males has been associated with a greater risk of developing dementia, however this study only included a sample of males, conversely other work revealed that both males and females with hypertension had greater risk of AD, but they did not perform a sex-specific analysis. By disentangling and identifying sex-specific RF underlying neurodegenerative diseases, targeted treatment of RF could be possible with a novel framework to conceptualize treatment. Study Aim/Hypothesis: Here, we will identify sex-based RF that contribute most significantly to AD. A network analysis will be employed in R to: i) reveal sex-specific nodes (i.e., RF); ii) sex-specific edges (i.e., relationships between RF). It is hypothesized that females will have more nodes relating to AD at baseline, including the APOE ɛ4 allele, and greater edges, representing a system with more closeness and betweenness. Conversely, males will have fewer nodes present, with reduced edges, representing lower closeness and betweenness. Proposed design: Cross-sectional and longitudinal where applicable. Participant cohorts: normal controls, mild cognitive impairment, those with early AD. Outcome Variables : All MRI scans and sequences, all cognitive assessments, genomics, medical history, smoking history, biospecimen results, leisure activities, neuropsychiatric inventory, anxiety questionnaires, depression questionnaires, physical measurements, falls history, mobility outcomes, details of disease onset and progression, family history, medications, questionnaires Independent Variables : sex and diagnosis Covariates: Education and Age |
| Additional Investigators | |
| Investigator's Name: | Sandra Black |
| Proposed Analysis: | Title: Sex-specific risk factors during aging and the dementia spectrum: an in depth understanding using network analysis Brief description: The number of individual's with Alzheimer's disease, is on the rise and will continue to increase as the number of older adults that comprise our populations are expected to continue to climb. Identifying risk factors that contribute most and the interaction of them, is necessary to have efficacious preventative and interventional strategies. Importantly, sex is well documented to have significant effects on physiology throughout the lifespan, yet in the context of risk factors in the aging and dementia spectrum is fairly understudied. When it is involved in an analysis, sex-aggregated analyses (i.e., employing sex as a covariate) are utilized, rather than employing sex-disaggregated analyses. Here, we propose to employ a network approach to identify how males and females risk factors across the spectrum of dementia differ and overlap. Background: The risk of being diagnosed with a neurodegenerative disease, like Alzheimer's disease (AD) increases with aging due to an assortment of modifiable and non-modifiable risk factors. Risk factors (RF) for AD include age, genetics, vascular risk factors, to name a few. Sex differences for the risk factors of AD are not well characterized, as most studies use sex as a covariate, rather than sex-disaggregated analyses. For example, hypertension in males has been associated with a greater risk of developing dementia, however this study only included a sample of males, conversely other work revealed that both males and females with hypertension had greater risk of AD, but they did not perform a sex-specific analysis. By disentangling and identifying sex-specific RF underlying neurodegenerative diseases, targeted treatment of RF could be possible with a novel framework to conceptualize treatment. Study Aim/Hypothesis: Here, we will identify sex-based RF that contribute most significantly to AD. A network analysis will be employed in R to: i) reveal sex-specific nodes (i.e., RF); ii) sex-specific edges (i.e., relationships between RF). It is hypothesized that females will have more nodes relating to AD at baseline, including the APOE ɛ4 allele, and greater edges, representing a system with more closeness and betweenness. Conversely, males will have fewer nodes present, with reduced edges, representing lower closeness and betweenness. Proposed design: Cross-sectional and longitudinal where applicable. Participant cohorts: normal controls, mild cognitive impairment, those with early AD. Outcome Variables : All MRI scans and sequences, all cognitive assessments, genomics, medical history, smoking history, biospecimen results, leisure activities, neuropsychiatric inventory, anxiety questionnaires, depression questionnaires, physical measurements, falls history, mobility outcomes, details of disease onset and progression, family history, medications, questionnaires Independent Variables : sex and diagnosis Covariates: Education and Age |
